Journal of Management in Engineering · 2020 · 139 citations · 47 references
EngineeringCommunicationDisaster DetectionDisaster CoverageBuilt EnvironmentGeographic Information SystemsSocial MediaData ScienceContent AnalysisDigital TwinsGeographyArtsDisaster ResponseTextual–visual–geo FrameworkEmergency CommunicationSocial Medium VisualizationDisaster ManagementBuilt Environment DisruptionsDisaster ResearchRemote SensingHuman-computer InteractionSocial SensingCrisis ManagementDisaster Risk ReductionFlood Risk ManagementDisaster Studies
Disaster response demands reliable situational awareness of infrastructure disruptions and societal impacts, but conventional remote sensing is insufficient for local‑scale events, so social sensing of user‑generated social media data is needed to monitor such disruptions. The study proposes and tests an integrated textual–visual–geo framework that uses images, texts, and geo‑maps from social media to detect infrastructure disruptions in smart city digital twins during disasters. The framework employs a graph‑based tweet detection, an image‑ranking algorithm, and a kernel density estimate to locate disruptions, and was applied to Houston’s flood‑control reservoir releases during Hurricane Harvey. The framework successfully captured critical situational information, enabling improved situational awareness and response, and demonstrated that integrating social sensing into smart city digital twins enhances stakeholders’ ability to cope with built‑environment disruptions during disasters.
This paper proposed and tested an integrated textual–visual–geo framework to enhance social sensing techniques in smart city digital twins in the context of disasters. Effective and efficient disaster response and recovery require reliable situational awareness regarding infrastructure disruptions and their societal impacts. Due to the rapid unfolding and evolution of events in disasters and emergencies, typical data sensing techniques (such as remote sensing and satellite images) are not sufficient to gain reliable situational awareness about disruptions that affect communities at a local scale. Social sensing enables gathering and analyzing massive user-generated data from various sources (social media, in particular) to monitor unfolding of localized events such as infrastructure disruptions and community needs. To advance social sensing methods and their integration into digital twins of cities, this study proposes an integrated framework for detecting infrastructure disruptions based on three information elements embedded in social media content: images, texts, and geo-maps. The framework consists of three main methods: a graph-based approach for detecting critical tweets, an image-ranking algorithm for selecting important images, and a kernel density estimate for estimating the geographical scales of the disruptions. The application of the proposed framework was demonstrated in a case study of water release from flood control reservoirs in Houston during Hurricane Harvey in 2017. The findings illustrate the capabilities of the proposed framework for capturing the critical situational information and interpreting the results for situational awareness and disruption response. The proposed framework can enhance integration of social sensing elements into smart city digital twins in the context of disasters. Accordingly, the proposed framework can improve the ability of community members, volunteer responders, residents, and other stakeholders in coping with built environment disruptions in disasters.
47
Rapid assessment of disaster damage using social media activity
Yury Kryvasheyeu, Haohui Chen, Nick Obradovich et al. · Science Advances · 2016 · 618 citations · Full text